from datetime import timedelta

from utils.operators.cluster_for_spark_sql_operator import SparkSqlOperator

jms_tmp__tmp_wide_sign_summary_waybill_dt_back_up = SparkSqlOperator(
    task_id='jms_tmp__tmp_wide_sign_summary_waybill_dt_back_up',
    task_concurrency=1,
    pool_slots=5,
    master='yarn',
    name='jms_tmp__tmp_wide_sign_summary_waybill_dt_back_up_{{ execution_date | date_add(1) | cst_ds }}',
    sql='jms_data_back_up/dwd/wide/tmp_wide_sign_summary_waybill_dt_back_up/execute.sql',
    executor_cores=5,
    executor_memory='15G',
    email=['rongguangfan@jtexpress.com','yl_bigdata@yl-scm.com'],
    num_executors=50,  # spark.dynamicAllocation.enabled 为 True 时，num_executors 表示最少 Executor 数
    conf={'spark.dynamicAllocation.enabled': 'true',  # 动态资源开启
          'spark.shuffle.service.enabled': 'true',  # 动态资源 Shuffle 服务开启
          'spark.dynamicAllocation.maxExecutors': 120,  # 动态资源最大扩容 Executor 数
          'spark.dynamicAllocation.cachedExecutorIdleTimeout': 60,  # 动态资源自动释放闲置 Executor 的超时时间(s)
          'spark.sql.sources.partitionOverwriteMode': 'dynamic',  # 允许删改已存在的分区
          'spark.executor.memoryOverhead': '6G',  # 堆外内存
          'spark.sql.shuffle.partitions': 3000,
          'spark.default.paralleism': 3000,
          },
    hiveconf={'hive.exec.dynamic.partition': 'true',  # 动态分区
              'hive.exec.dynamic.partition.mode': 'nonstrict',
              'hive.exec.max.dynamic.partitions': 200,  # 每天生成 200 个分区
              'hive.exec.max.dynamic.partitions.pernode': 200,  # 每天生成 20 个分区
              },
    yarn_queue='pro',
    execution_timeout=timedelta(minutes=90),
)